Anomaly detection stands as a crucial aspect of time series analysis, aiming to identify abnormal events in time series samples. The central challenge of this task lies in effectively learning the representations of normal and abnormal patterns in a label-lacking scenario. Previous research mostly relied on reconstruction-based approaches, restricting the representational abilities of the models. In addition, most of the current deep learning-based methods are not lightweight enough, which prompts us to design a more efficient framework for anomaly detection. In this study, we introduce PatchAD, a novel multi-scale patch-based MLP-Mixer architecture that leverages contrastive learning for representational extraction and anomaly detection. Specifically, PatchAD is composed of four distinct MLP Mixers, exclusively utilizing the MLP architecture for high efficiency and lightweight architecture. Additionally, we also innovatively crafted a dual project constraint module to mitigate potential model degradation. Comprehensive experiments demonstrate that PatchAD achieves state-of-the-art results across multiple real-world multivariate time series datasets. Our code is publicly available.\footnote{\url{https://github.com/EmorZz1G/PatchAD}}

翻译：[translated abstract in Chinese] 异常检测是时间序列分析的关键方面之一，旨在识别时间序列样本中的异常事件。该任务的核心挑战在于如何在缺乏标签的情况下有效学习正常与异常模式的表征。此前研究多依赖基于重构的方法，限制了模型的表征能力。此外，当前多数基于深度学习的方法缺乏轻量化特性，这促使我们设计更高效的异常检测框架。本研究提出PatchAD——一种新颖的多尺度补丁型MLP-Mixer架构，利用对比学习进行表征提取与异常检测。具体而言，PatchAD由四个独立的MLP Mixers构成，完全采用MLP架构以实现高效轻量化设计。同时，我们创新性地设计了双投影约束模块以缓解潜在的模型退化问题。大量实验表明，PatchAD在多个真实多变量时间序列数据集上取得了最先进的性能。我们的代码已公开。\footnote{\url{https://github.com/EmorZz1G/PatchAD}}